{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vr9-SPbQxLvu"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Trusted-AI/AIF360/blob/main/examples/sklearn/demo_learning_fair_representations.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qf0ZkGCab5DM"
      },
      "source": [
        "# Learning Fair Representations\n",
        "\n",
        "The Learned Fair Representations method (LFR) aims to learn an encoding of the data which preserves as much information as possible while removing protected information. It does this by forming a probabilistic mapping of the data to a set of \"prototypes\" (clusters). The learned representation can be used by a downstream estimator (pre-processing) or as a linear model itself, using weights learned in the encoding process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StDlibZvb6Xn",
        "outputId": "f01bc578-64ad-4fd7-ebc2-bc2133b98029"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Collecting tempeh\n",
            "  Downloading tempeh-0.1.12-py3-none-any.whl (39 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->aif360[LawSchoolGPA]) (2.8.2)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->aif360[LawSchoolGPA]) (0.11.0)\n",
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            "Collecting memory-profiler\n",
            "  Downloading memory_profiler-0.60.0.tar.gz (38 kB)\n",
            "Requirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from tempeh->aif360[LawSchoolGPA]) (3.6.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from tempeh->aif360[LawSchoolGPA]) (2.23.0)\n",
            "Collecting shap\n",
            "  Downloading shap-0.41.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (569 kB)\n",
            "\u001b[K     |████████████████████████████████| 569 kB 10.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from memory-profiler->tempeh->aif360[LawSchoolGPA]) (5.4.8)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest->tempeh->aif360[LawSchoolGPA]) (1.11.0)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->tempeh->aif360[LawSchoolGPA]) (2.10)\n",
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            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.7/dist-packages (from shap->tempeh->aif360[LawSchoolGPA]) (21.3)\n",
            "Collecting slicer==0.0.7\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from shap->tempeh->aif360[LawSchoolGPA]) (1.5.0)\n",
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            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba->shap->tempeh->aif360[LawSchoolGPA]) (4.12.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba->shap->tempeh->aif360[LawSchoolGPA]) (3.8.1)\n",
            "Building wheels for collected packages: memory-profiler\n",
            "  Building wheel for memory-profiler (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for memory-profiler: filename=memory_profiler-0.60.0-py3-none-any.whl size=31284 sha256=1efe7d12e7027290e2d6f430d592a507e62c9f548682b6439a8dd24dc2ce35d3\n",
            "  Stored in directory: /root/.cache/pip/wheels/67/2b/fb/326e30d638c538e69a5eb0aa47f4223d979f502bbdb403950f\n",
            "Successfully built memory-profiler\n",
            "Installing collected packages: slicer, shap, memory-profiler, tempeh\n",
            "Successfully installed memory-profiler-0.60.0 shap-0.41.0 slicer-0.0.7 tempeh-0.1.12\n"
          ]
        }
      ],
      "source": [
        "# Install AIF360\n",
        "!pip install 'aif360'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "nBED8LJPb5DP",
        "tags": []
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "pd.set_option('display.max_columns', None)\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import accuracy_score, balanced_accuracy_score\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "from aif360.sklearn.datasets import fetch_adult\n",
        "from aif360.sklearn.metrics import make_scorer, statistical_parity_difference, consistency_score\n",
        "from aif360.sklearn.preprocessing import LearnedFairRepresentations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nbi06MU1b5DQ"
      },
      "source": [
        "Fetch Adult dataset, encode categorical features, and split into train/test."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fHE8ZfPLb5DQ",
        "outputId": "60096f77-0b23-4183-a3e6-6f8ee2749033"
      },
      "outputs": [
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17612</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <td>20.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17613</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <td>23.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17614</th>\n",
              "      <th>1</th>\n",
              "      <th>1</th>\n",
              "      <td>57.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17615</th>\n",
              "      <th>1</th>\n",
              "      <th>1</th>\n",
              "      <td>51.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48837</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <td>27.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "</table>\n",
              "<p>28941 rows × 93 columns</p>\n",
              "</div>"
            ],
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              "48840 1    1                            0                                           0                     0                            0                       0                         0                            0                         0                      0                       0   \n",
              "48841 1    0                            0                                           0                     0                            0                       0                         0                            0                         0                      0                       0   \n",
              "\n",
              "                native-country__Thailand  native-country__Trinadad&Tobago  native-country__United-States  native-country__Vietnam  native-country__Yugoslavia  \n",
              "      race sex                                                                                                                                                 \n",
              "17611 1    1                           0                                0                              1                        0                           0  \n",
              "17612 1    0                           0                                0                              1                        0                           0  \n",
              "17613 0    1                           0                                0                              1                        0                           0  \n",
              "17614 1    1                           0                                0                              1                        0                           0  \n",
              "17615 1    1                           0                                0                              1                        0                           0  \n",
              "...                                  ...                              ...                            ...                      ...                         ...  \n",
              "48837 1    0                           0                                0                              1                        0                           0  \n",
              "48838 1    1                           0                                0                              1                        0                           0  \n",
              "48839 1    0                           0                                0                              1                        0                           0  \n",
              "48840 1    1                           0                                0                              1                        0                           0  \n",
              "48841 1    0                           0                                0                              1                        0                           0  \n",
              "\n",
              "[28941 rows x 93 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X, y, _ = fetch_adult()\n",
        "\n",
        "X.index = y.index = pd.MultiIndex.from_arrays(X.index.codes, names=X.index.names)\n",
        "y = pd.Series(y.factorize(sort=True)[0], index=y.index, name=y.name)\n",
        "X = pd.get_dummies(X, prefix_sep='__', drop_first=True)\n",
        "\n",
        "X_test, X_train, y_test, y_train = train_test_split(X, y, train_size=16281, shuffle=False)\n",
        "X_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9VUdZlaab5DR"
      },
      "source": [
        "Create some scoring functions we can use in a grid search. We will use the balanced accuracy version of the delta function to avoid getting stuck in the case of predicting all zeros."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o5uqDRjNb5DS"
      },
      "outputs": [],
      "source": [
        "from sklearn import metrics as skm\n",
        "\n",
        "def discrimination(y_true, y_pred):\n",
        "    return abs(statistical_parity_difference(y_true, y_pred, prot_attr='sex'))\n",
        "\n",
        "def delta(y_true, y_pred, use_bal_acc=False):\n",
        "    if use_bal_acc:\n",
        "        return balanced_accuracy_score(y_true, y_pred) - discrimination(y_true, y_pred)\n",
        "    else:\n",
        "        return accuracy_score(y_true, y_pred) - discrimination(y_true, y_pred)\n",
        "\n",
        "min_disc = make_scorer(statistical_parity_difference, prot_attr='sex')\n",
        "max_delta = skm.make_scorer(delta, use_bal_acc=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFzrHhcyb5DS"
      },
      "source": [
        "Use a grid search to find the best model. Note: this takes a while to run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5mz1feJCb5DT",
        "outputId": "bc853fe7-ed6c-4eec-a7e3-83e07288870a",
        "scrolled": false,
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_fairness_weight</th>\n",
              "      <th>param_reconstruct_weight</th>\n",
              "      <th>param_target_weight</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>180.131182</td>\n",
              "      <td>124.216351</td>\n",
              "      <td>0.101405</td>\n",
              "      <td>0.010371</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>100</td>\n",
              "      <td>{'fairness_weight': 0, 'reconstruct_weight': 0...</td>\n",
              "      <td>0.556035</td>\n",
              "      <td>0.570426</td>\n",
              "      <td>0.559688</td>\n",
              "      <td>0.562050</td>\n",
              "      <td>0.006108</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>817.467676</td>\n",
              "      <td>90.755964</td>\n",
              "      <td>0.131997</td>\n",
              "      <td>0.006352</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'fairness_weight': 0, 'reconstruct_weight': 0...</td>\n",
              "      <td>0.569775</td>\n",
              "      <td>0.569911</td>\n",
              "      <td>0.568105</td>\n",
              "      <td>0.569264</td>\n",
              "      <td>0.000821</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>317.998187</td>\n",
              "      <td>306.160906</td>\n",
              "      <td>0.111459</td>\n",
              "      <td>0.012950</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>100</td>\n",
              "      <td>{'fairness_weight': 0, 'reconstruct_weight': 0...</td>\n",
              "      <td>0.549142</td>\n",
              "      <td>0.568866</td>\n",
              "      <td>0.557476</td>\n",
              "      <td>0.558495</td>\n",
              "      <td>0.008085</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>764.726667</td>\n",
              "      <td>14.500247</td>\n",
              "      <td>0.120949</td>\n",
              "      <td>0.009582</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'fairness_weight': 0, 'reconstruct_weight': 0...</td>\n",
              "      <td>0.557025</td>\n",
              "      <td>0.581762</td>\n",
              "      <td>0.583586</td>\n",
              "      <td>0.574125</td>\n",
              "      <td>0.012114</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>599.155342</td>\n",
              "      <td>299.293781</td>\n",
              "      <td>0.121888</td>\n",
              "      <td>0.009188</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>100</td>\n",
              "      <td>{'fairness_weight': 0, 'reconstruct_weight': 0...</td>\n",
              "      <td>0.558752</td>\n",
              "      <td>0.591082</td>\n",
              "      <td>0.574132</td>\n",
              "      <td>0.574655</td>\n",
              "      <td>0.013204</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>844.970299</td>\n",
              "      <td>17.113430</td>\n",
              "      <td>0.137907</td>\n",
              "      <td>0.012667</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'fairness_weight': 0, 'reconstruct_weight': 0...</td>\n",
              "      <td>0.568116</td>\n",
              "      <td>0.583722</td>\n",
              "      <td>0.580975</td>\n",
              "      <td>0.577604</td>\n",
              "      <td>0.006803</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>553.185381</td>\n",
              "      <td>387.464559</td>\n",
              "      <td>0.114644</td>\n",
              "      <td>0.010752</td>\n",
              "      <td>100</td>\n",
              "      <td>0.01</td>\n",
              "      <td>100</td>\n",
              "      <td>{'fairness_weight': 100, 'reconstruct_weight':...</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.557818</td>\n",
              "      <td>0.547013</td>\n",
              "      <td>0.534944</td>\n",
              "      <td>0.025099</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>873.064806</td>\n",
              "      <td>26.249930</td>\n",
              "      <td>0.173718</td>\n",
              "      <td>0.065214</td>\n",
              "      <td>100</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'fairness_weight': 100, 'reconstruct_weight':...</td>\n",
              "      <td>0.564684</td>\n",
              "      <td>0.579914</td>\n",
              "      <td>0.587183</td>\n",
              "      <td>0.577261</td>\n",
              "      <td>0.009375</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>770.201270</td>\n",
              "      <td>109.835104</td>\n",
              "      <td>0.122977</td>\n",
              "      <td>0.018031</td>\n",
              "      <td>100</td>\n",
              "      <td>0.001</td>\n",
              "      <td>100</td>\n",
              "      <td>{'fairness_weight': 100, 'reconstruct_weight':...</td>\n",
              "      <td>0.583923</td>\n",
              "      <td>0.567010</td>\n",
              "      <td>0.578141</td>\n",
              "      <td>0.576358</td>\n",
              "      <td>0.007019</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>757.053957</td>\n",
              "      <td>55.965935</td>\n",
              "      <td>0.119703</td>\n",
              "      <td>0.009574</td>\n",
              "      <td>100</td>\n",
              "      <td>0.001</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'fairness_weight': 100, 'reconstruct_weight':...</td>\n",
              "      <td>0.565330</td>\n",
              "      <td>0.588186</td>\n",
              "      <td>0.582913</td>\n",
              "      <td>0.578810</td>\n",
              "      <td>0.009772</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>529.183054</td>\n",
              "      <td>349.084901</td>\n",
              "      <td>0.133338</td>\n",
              "      <td>0.014336</td>\n",
              "      <td>100</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>100</td>\n",
              "      <td>{'fairness_weight': 100, 'reconstruct_weight':...</td>\n",
              "      <td>0.563342</td>\n",
              "      <td>0.554957</td>\n",
              "      <td>0.570223</td>\n",
              "      <td>0.562841</td>\n",
              "      <td>0.006242</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>803.389990</td>\n",
              "      <td>26.519410</td>\n",
              "      <td>0.121788</td>\n",
              "      <td>0.002154</td>\n",
              "      <td>100</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'fairness_weight': 100, 'reconstruct_weight':...</td>\n",
              "      <td>0.573397</td>\n",
              "      <td>0.594797</td>\n",
              "      <td>0.582037</td>\n",
              "      <td>0.583410</td>\n",
              "      <td>0.008790</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>180.188542</td>\n",
              "      <td>126.965618</td>\n",
              "      <td>0.128179</td>\n",
              "      <td>0.008648</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.01</td>\n",
              "      <td>100</td>\n",
              "      <td>{'fairness_weight': 1000, 'reconstruct_weight'...</td>\n",
              "      <td>0.561373</td>\n",
              "      <td>0.506304</td>\n",
              "      <td>0.610161</td>\n",
              "      <td>0.559280</td>\n",
              "      <td>0.042425</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>811.806859</td>\n",
              "      <td>16.705233</td>\n",
              "      <td>0.124774</td>\n",
              "      <td>0.012793</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'fairness_weight': 1000, 'reconstruct_weight'...</td>\n",
              "      <td>0.577256</td>\n",
              "      <td>0.599278</td>\n",
              "      <td>0.595564</td>\n",
              "      <td>0.590699</td>\n",
              "      <td>0.009626</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>99.372340</td>\n",
              "      <td>9.106103</td>\n",
              "      <td>0.141386</td>\n",
              "      <td>0.014610</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001</td>\n",
              "      <td>100</td>\n",
              "      <td>{'fairness_weight': 1000, 'reconstruct_weight'...</td>\n",
              "      <td>0.564451</td>\n",
              "      <td>0.571415</td>\n",
              "      <td>0.546781</td>\n",
              "      <td>0.560882</td>\n",
              "      <td>0.010369</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>752.759500</td>\n",
              "      <td>29.864239</td>\n",
              "      <td>0.096864</td>\n",
              "      <td>0.008705</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.001</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'fairness_weight': 1000, 'reconstruct_weight'...</td>\n",
              "      <td>0.576862</td>\n",
              "      <td>0.595392</td>\n",
              "      <td>0.592596</td>\n",
              "      <td>0.588283</td>\n",
              "      <td>0.008156</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>336.636596</td>\n",
              "      <td>25.680870</td>\n",
              "      <td>0.110729</td>\n",
              "      <td>0.013070</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>100</td>\n",
              "      <td>{'fairness_weight': 1000, 'reconstruct_weight'...</td>\n",
              "      <td>0.612521</td>\n",
              "      <td>0.641050</td>\n",
              "      <td>0.642899</td>\n",
              "      <td>0.632157</td>\n",
              "      <td>0.013905</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>308.082460</td>\n",
              "      <td>157.998862</td>\n",
              "      <td>0.079211</td>\n",
              "      <td>0.034830</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'fairness_weight': 1000, 'reconstruct_weight'...</td>\n",
              "      <td>0.575052</td>\n",
              "      <td>0.569575</td>\n",
              "      <td>0.540778</td>\n",
              "      <td>0.561802</td>\n",
              "      <td>0.015033</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_fairness_weight param_reconstruct_weight param_target_weight                                             params  split0_test_score  split1_test_score  split2_test_score  mean_test_score  std_test_score  rank_test_score\n",
              "0      180.131182    124.216351         0.101405        0.010371                     0                     0.01                 100  {'fairness_weight': 0, 'reconstruct_weight': 0...           0.556035           0.570426           0.559688         0.562050        0.006108               13\n",
              "1      817.467676     90.755964         0.131997        0.006352                     0                     0.01                1000  {'fairness_weight': 0, 'reconstruct_weight': 0...           0.569775           0.569911           0.568105         0.569264        0.000821               11\n",
              "2      317.998187    306.160906         0.111459        0.012950                     0                    0.001                 100  {'fairness_weight': 0, 'reconstruct_weight': 0...           0.549142           0.568866           0.557476         0.558495        0.008085               17\n",
              "3      764.726667     14.500247         0.120949        0.009582                     0                    0.001                1000  {'fairness_weight': 0, 'reconstruct_weight': 0...           0.557025           0.581762           0.583586         0.574125        0.012114               10\n",
              "4      599.155342    299.293781         0.121888        0.009188                     0                   0.0001                 100  {'fairness_weight': 0, 'reconstruct_weight': 0...           0.558752           0.591082           0.574132         0.574655        0.013204                9\n",
              "5      844.970299     17.113430         0.137907        0.012667                     0                   0.0001                1000  {'fairness_weight': 0, 'reconstruct_weight': 0...           0.568116           0.583722           0.580975         0.577604        0.006803                6\n",
              "6      553.185381    387.464559         0.114644        0.010752                   100                     0.01                 100  {'fairness_weight': 100, 'reconstruct_weight':...           0.500000           0.557818           0.547013         0.534944        0.025099               18\n",
              "7      873.064806     26.249930         0.173718        0.065214                   100                     0.01                1000  {'fairness_weight': 100, 'reconstruct_weight':...           0.564684           0.579914           0.587183         0.577261        0.009375                7\n",
              "8      770.201270    109.835104         0.122977        0.018031                   100                    0.001                 100  {'fairness_weight': 100, 'reconstruct_weight':...           0.583923           0.567010           0.578141         0.576358        0.007019                8\n",
              "9      757.053957     55.965935         0.119703        0.009574                   100                    0.001                1000  {'fairness_weight': 100, 'reconstruct_weight':...           0.565330           0.588186           0.582913         0.578810        0.009772                5\n",
              "10     529.183054    349.084901         0.133338        0.014336                   100                   0.0001                 100  {'fairness_weight': 100, 'reconstruct_weight':...           0.563342           0.554957           0.570223         0.562841        0.006242               12\n",
              "11     803.389990     26.519410         0.121788        0.002154                   100                   0.0001                1000  {'fairness_weight': 100, 'reconstruct_weight':...           0.573397           0.594797           0.582037         0.583410        0.008790                4\n",
              "12     180.188542    126.965618         0.128179        0.008648                  1000                     0.01                 100  {'fairness_weight': 1000, 'reconstruct_weight'...           0.561373           0.506304           0.610161         0.559280        0.042425               16\n",
              "13     811.806859     16.705233         0.124774        0.012793                  1000                     0.01                1000  {'fairness_weight': 1000, 'reconstruct_weight'...           0.577256           0.599278           0.595564         0.590699        0.009626                2\n",
              "14      99.372340      9.106103         0.141386        0.014610                  1000                    0.001                 100  {'fairness_weight': 1000, 'reconstruct_weight'...           0.564451           0.571415           0.546781         0.560882        0.010369               15\n",
              "15     752.759500     29.864239         0.096864        0.008705                  1000                    0.001                1000  {'fairness_weight': 1000, 'reconstruct_weight'...           0.576862           0.595392           0.592596         0.588283        0.008156                3\n",
              "16     336.636596     25.680870         0.110729        0.013070                  1000                   0.0001                 100  {'fairness_weight': 1000, 'reconstruct_weight'...           0.612521           0.641050           0.642899         0.632157        0.013905                1\n",
              "17     308.082460    157.998862         0.079211        0.034830                  1000                   0.0001                1000  {'fairness_weight': 1000, 'reconstruct_weight'...           0.575052           0.569575           0.540778         0.561802        0.015033               14"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lfr = LearnedFairRepresentations('sex', n_prototypes=25, max_iter=1000, random_state=1234)\n",
        "params = {'reconstruct_weight': [1e-2, 1e-3, 1e-4],\n",
        "          'target_weight': [100, 1000],\n",
        "          'fairness_weight': [0, 100, 1000]}\n",
        "grid = GridSearchCV(lfr, params, scoring=max_delta, cv=3, n_jobs=-1).fit(X_train, y_train)\n",
        "res = pd.DataFrame(grid.cv_results_)\n",
        "res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f6I8MXOkb5DT",
        "outputId": "164cf338-1c4a-4742-acd9-b7af9b7d6b14",
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1728x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "f, axes = plt.subplots(1, 3, squeeze=True, figsize=(24, 6))\n",
        "by_fair = res.set_index('param_fairness_weight')\n",
        "for ax, r in zip(axes, by_fair.index.unique()):\n",
        "    sns.heatmap(by_fair.xs(r).pivot('param_reconstruct_weight', 'param_target_weight', 'mean_test_score'),\n",
        "                annot=True, fmt='.1%', vmin=res.mean_test_score.min(), vmax=res.mean_test_score.max(), square=True, cbar=False, ax=ax);\n",
        "    ax.set_title('param_fairness_weight={}'.format(r))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1Iooynbb5DU"
      },
      "source": [
        "Compare predictions from naive logistic regression, LFR (in-processing), and LFR (pre-processing) with a logistic regression classifier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EbYOLCsCb5DU"
      },
      "outputs": [],
      "source": [
        "y_pred_lfr = grid.predict(X_test)\n",
        "lr = LogisticRegression(solver='lbfgs', random_state=123)\n",
        "y_pred_lr = lr.fit(X_train, y_train).predict(X_test)\n",
        "y_pred_lfr_lr = lr.fit(grid.transform(X_train), y_train).predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "myneuMWeb5DU"
      },
      "outputs": [],
      "source": [
        "# https://matplotlib.org/3.3.0/gallery/lines_bars_and_markers/barchart.html\n",
        "def autolabel(rects):\n",
        "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
        "    for rect in rects:\n",
        "        height = rect.get_height()\n",
        "        plt.annotate('{:.1%}'.format(height),\n",
        "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
        "                    xytext=(0, 3),  # 3 points vertical offset\n",
        "                    textcoords=\"offset points\",\n",
        "                    ha='center', va='bottom')\n",
        "\n",
        "def plot_metrics(y_pred_lr, y_pred_lfr, y_pred_lfr_lr):\n",
        "    f = plt.figure(figsize=(12, 6))\n",
        "\n",
        "    preds = [y_pred_lr, y_pred_lfr, y_pred_lfr_lr]\n",
        "    colors = ['y', 'b', 'm']\n",
        "    labels = ['LR', 'LFR', 'LFR+LR']\n",
        "    i = 0\n",
        "    for y_pred, c, label in zip(preds, colors, labels):\n",
        "        autolabel(plt.bar([i], [accuracy_score(y_test, y_pred)], color=c, label=label))\n",
        "        i += 1\n",
        "    i += 1.5\n",
        "    for y_pred, c in zip(preds, colors):\n",
        "        autolabel(plt.bar([i], [balanced_accuracy_score(y_test, y_pred)], color=c))\n",
        "        i += 1\n",
        "    i += 1.5\n",
        "    for y_pred, c in zip(preds, colors):\n",
        "        autolabel(plt.bar([i], [discrimination(y_test, y_pred)], color=c))\n",
        "        i += 1\n",
        "    i += 1.5\n",
        "    for y_pred, c in zip(preds, colors):\n",
        "        autolabel(plt.bar([i], [consistency_score(X_test, y_pred)], color=c))\n",
        "        i += 1\n",
        "\n",
        "    plt.xlabel('Adult')\n",
        "    plt.xticks([1, 5.5, 10, 14.5], ['Accuracy', 'Balanced Accuracy', 'Discrimination', 'Consistency'])\n",
        "    plt.ylim([0, 1.1])\n",
        "    plt.legend()\n",
        "    plt.close()\n",
        "    return f"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kJggeYNpb5DY",
        "outputId": "e2ff4158-ab19-4656-e536-5b3bd2f87098",
        "tags": []
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "plot_metrics(y_pred_lr, y_pred_lfr, y_pred_lfr_lr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQJF9JIVb5DZ"
      },
      "source": [
        "Feel free to try this out with other datasets as well!"
      ]
    }
  ],
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      "provenance": []
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